Greening the electric power generating system: application of optimization model and sectoral approach in Indonesia

Transcription

1 1 Greening the electric power generating system: application of optimization model and sectoral approach in Indonesia By Maxensius Tri Sambodo Economic Research Center Indonesian Institute of Sciences

2 2 What is greening? (Jisoon Lee, 2010) Green growth: Korean Initiative for Green Civilization 1. refer to activities that would reduce the per capita pollution. 2. has to do with coming up with new ways of doing things 3. The key here is new ideas, technologies, and knowhow 4. Green growth greening results in increases in the value added

3 3 Outline 1. Background and Research Objectives 2. Current Power Generating System in Java-Bali 3. Data, Scenarios and Methodology 4. Formulation of Mathematical Model 5. Numerical Results and Sensitivity Analyses 6. Conclusions, Policy Implications and Future Study

4 1. Background and Research Objectives 4

5 5 Share of CO 2 emissions as total share of energy sector in 2007, in Indonesia Share of CO 2 emissions as total share of energy sector in 2007, in Indonesia Source: World Resources Institute, 2012

6 6 Fossil fuel consumption to produce electricity in Indonesia Increasing share of oil Shrinking share of natural gas Coal becomes the backbone of power system Source: Indonesian Energy Balance Table (Ministry of Energy and Mineral Resources) original unit in BOE; indicates energy in-take during the transformation

7 7 Commitments to reduce CO 2 emissions 1. Clean Development Mechanism (CDM) the energy sector between 2007 and 2020, we obtained 31 projects on the list with total CO 2 reductions of about 35.2 million tons. Geothermal and biomass projects show the highest reduction in emission targets, followed by the cement industry. 2. Copenhagen accord - National Action Plan (NAP) for the Greenhouse Gas Emission Reduction Government Regulation No.61 Year Between 2010 and 2020, supply side (micro and mini hydro, solar panel, wind and biomass), approximately 4.4 million ton, respectively. The demand side efficiency will also save electricity, total CO 2 emissions reduction obtained from household utensils efficiency improvement is about Thus following the regulation, total CO 2 emissions reduction from both supply and demand sides will amount to about million ton. 3. Feed in tariff for renewable energy

8 8 Feed in Tariff (FIT) Base on the Ministry of Energy and Mineral Resources Regulation (MEMR) No 4/2012 Installed Capacity of renewable energy up to 10 MW Renewable energy in general 1.If it is connected to low voltage (1,004/kWh X F) = 11.2 cent US$/KWh 2.If it us connected to medium voltage (656/kWh) X F) Biomass & Biogas 1.If it is connected to low voltage (1,325/kWh X F) 2.If it us connected to medium voltage (975/kWh X F) City Waste (Zero Waste) 1.If it is connected to low voltage (1,398/kWh X F) 2.If it us connected to medium voltage (1,050/kWh X F) City Waste (Sanitary landfill) 1.If it is connected to low voltage (1,198/kWh X F) 2.If it us connected to medium voltage (850/kWh X F) F is incentive factor that base on region: 1.Java-Madura-Bali-Sumatera; F = 1 2.Sulawesi, Kalimantan, NTT, and NTB; F = Maluku and Papua; F = 1.3 IN JAVA ONLY MIN-FIT = 7.3 cent US$/kWh MAX-FIT = 15.5 cent US$/kWh According to MEMR Regulation No 2/2011, for Geothermal the maximum FIT is 9.70 cent US$/kWh (for high voltage connection) min with capacity at least 30 MW (mostly)

9 9 Alternative channel 1. The Kyoto Protocol is ended in Sectoral Approach (SA) as a post Kyoto framework. 2. SA was created in the Bali Action Plan as a cooperative sectoral approach (Sawa, 2008). 3. SA is designed for developing countries to meet voluntary no-lose GHG emissions target. No penalties-above target; and earn Emissions Reduction Credits (ERCs) sold to industrialized countries. 4. Schmidt et al. (2008) proposed electricity and 5 major industrial sectors such as iron and steel, chemical and petrochemical, aluminum, cement and limestone, and paper, pulp and printing as a candidate of sectoral approaches.

10 10 Questions 1. Can Indonesia optimize the SA base on the existing power planning? 2. How does Indonesia optimize SA? 3. If SA will be implemented, what kinds of electric power policies are need to be prepared?

11 11 Sectoral Approach (SA) Sawa (2008), increase the average efficiency level of coal-fired power plants Experts assess and define energy intensity benchmarks in power sector to use as a starting point for discussions Non-Annex I countries pledge a carbon intensity level that they can meet without assistance Annex I countries negotiate with developing countries on specific financial and other support through a Technology Finance and Assistance Package to encourage non-annex I countries to ultimately commit to stricter no-lose emissions intensity levels.

12 12 Generating cost and emission intensity for new power plant No Description Unit Combine cycle Gas turbine Steam subcritical Steam supercritical Steam ultrasuper- Critical Geothermal Hydrolarge 1 Life Year Discount Rate % Capital Cost USD/kW ,200 1,400 1,600 3,350 2,000 4 Capacity MW ,000 1,000 1, CF % gas/natural gas gas/natural gas coal lignite coal lignite coal lignite Fuel Type (pure) Model Scenario Gas Price (coal price) USD/MMBTU (USD/ton) 6 6 (50) (50) (50) Heat Content KCal/Mscf 252, ,000 4,200 4,200 4, Efficiency (net, LHV) % Component Fuel Cost Million USD/Year A Capacity Charge Cent/kWh B O&M Fix Cent/kWh {2%x(inv.cost/prod.)} C Fuel Cost Cent/kWh D O&M Var Cent/kWh (3% from C) Total Cost Cent/kWh Total Cost Rp/kWh Carbon intensity tonco2/mwh

13 % 13 If fuel cost increase by 100% No Description Unit Gas Price (coal price) Possible Scenario II Combine cycle Gas turbine Steam subcritical Steam supercritical Steam ultrasuper- Critical USD/MMBTU (USD/ton) (100) (100) (100) Component Fuel cost Million USD/Year A Capacity Charge Cent/kWh B O&M Fix Cent/kWh C Fuel Cost Cent/kWh D O&M Var Cent/kWh Total Cost Cent/kWh Total Cost Rp/kWh , Percentage change in generating cost after fuel cost increase The level of efficiency is matter

14 2. Current Power Generating System in Java-Bali 14

15 15 Power generating indicators in 2010 No Indicators Java-Bali Indonesia Share Java-Bali to national system (%) 1 Installed Capacity (MW) 19,061 26, Rated Capacity (MW) 17,535 23, Peak load (MW) 18,109 24, Purchased from outside 4 PT.PLN (GWh) 31,902 38, Own Production (GWh) 97, , Production (netto) (GWh) 129, , Energy sold (GWh) 113, , Electrification ratio (%) Source: PLN Statistics 2010 Electricity demand in Java-Bali still grow because about 27% of households do not have access on PT.PLN s power grid. Java-Bali system obtains the highest share of capacity and production.

16 16 PT.PLN s Power Capacity in 2010 Installed capacity (MW) Combine Hydro Steam Gas turbine cycle Geothermal Diesel Java-Bali 2,399 8,020 2,114 6, Indonesia 3,523 9,452 3,224 6, ,268 Share of Java-Bali (%) Rated Capacity (MW) Java-Bali 2,331 7,464 1,939 5, Indonesia 3,434 8,652 2,793 6, ,072 Share of Java-Bali (%) Primary energy supply in Java-Bali depends on outside Java-Bali regions Regions outside Java-Bali depend on diesel power plant (high dependency on oil)

17 17 3. Data, Scenarios and Methodology

18 18 Electric Power Expansion and Sectoral Approach Minimizing generating cost Objectives Linear programming: power plant expansion model 1. Load duration curve 2. Emission intensity 3. Generating cost Data - Information 3. Capacity planning 4. Availability factor Constraints 1. Capacity constraints 2. Primary energy supply constraint (fossil and non fossil) 3. Demand satisfaction 4. Contract agreement (PT.PLN and IPP) (fossil and non-fossil) 5. Promoting renewable energy

19 19 Capacity Planning in Java-Bali Type of plant Existing capacity in 2009 (MW) Share in 2009 (%) Additional capacity (MW) Total Capacity 2019 (MW) Share in 2019 (%) Steam 9, ,625 31, Combine cycle 5, ,401 12, Gas turbine 1, ,800 3, Diesel Geothermal ,255 3, Hydro 3, ,142 6, Total 20, ,223 56, Increase marginally

20 20 Scenarios For new power plants Demand side management (DSM) Power consumption is reduced by 5% Power consumption is reduced by 10% Fuel cost Coal price increases by 100% Gas price increases by 100% Technology Steam coal subcritical Steam coal supercitical Steam coal ultrasupercritical We do not include oil price in this scenario, because new power plant will not consume oil The first ultrasupercritical will operate in 2017 with capacity 2 x 1,000 MW

21 4. Formulation of Mathematical Model 21

22 (1) Formulation Index Type of power plant: Before types old plant (i): steam, combine cycle, gas turbine, diesel; and (j): geothermal, and hydro-large After types new plant (k): steam, combine cycle, gas turbine; and (l): geothermal, hydro-large Producers: PT.PLN (state company) and IPP (independent power producer/private) p = load duration block, p = 1,, P (in given period); where p = 1 indicates peak hour and p = 5 shows base load 22

23 23 Parameters: TD p = duration of load block p in hours PD p = maximum power demand in MWh in a load block p VCF i = generating cost (Rp/MW-h) old fossil fuel power plant type i PT.PLN VCNF j = generating cost (Rp/MW-h) old non-fossil fuel power plant type j - PT.PLN VCFN k = generating cost (Rp/MW-h) new fossil fuel power plant type k PT.PLN VCNFN l = generating cost (Rp/MW-h) new for non-fossil fuel power plant type l - PT.PLN VCFP i = generating cost (Rp/MW-h) old fossil fuel power plant type i Private VCNFP j = generating cost (Rp/MW-h) old non-fossil fuel power plant type j - Private VCFNP k = generating cost (Rp/MW-h) new fossil fuel power plant type k Private VCNFNP l = generating cost (Rp/MW-h) new non-fossil fuel power plant type l - Private CEF i = capacity (MW) of existing old fossil fuel power plant type i PT.PLN CENF j = capacity (MW) of existing old non-fossil fuel power plant type j PT.PLN CEFP i = capacity (MW) of existing old fossil fuel power plant type i Private CENFP j = capacity (MW) of existing old non-fossil fuel power plant type j Private

24 24 Parameters: AFF i = availability factor for old fossil fuel power plant type i PT.PLN AFNF j = availability factor for old non-fossil fuel power plant type j PT.PLN AFFP i = availability factor for old fossil fuel power plant type i Private AFNFP j = availability factor for old non-fossil fuel power plant type j Private AFFN k = availability factor for new fossil fuel power plant type k PT.PLN AFNFN l = availability factor for new non-fossil fuel power plant type l PT.PLN AFFPN k = availability factor for new fossil fuel power plant type k Private AFNFP l = availability factor for new non-fossil fuel power plant type l Private ADDF k = additional capacity for new fossil fuel power plant type k PT.PLN ADDNF l = additional capacity for new non-fossil power plant type l PT.PLN ADDFP k = additional capacity for new fossil fuel power plant type k Private ADDNFP l = additional capacity for new non-fossil power plant type l Private

25 25 Decision variables Focus on Electricity production Old power plant New power plant Fossil Non-fossil Fossil Non-fossil PT.PLN IPP PT.PLN IPP PT.PLN IPP PT.PLN IPP We have eight decision variables

26 26 (2) Constraints a. Capacity constraints OutF ip OutNF jp OutNEWNF lp OutNFP jp AFF i OutNEWF kp OutFP ip OutNEWFP kp CEF TD i p AFNF CENF TD j j p OutNEWNFP lp AFFP i AFNF l AFNFP j AFF k AFNFP l ADDNF l TD p CENFP TD j p ADDF k CEFP TD i p AFFP k ADDFP k ADDNFP l for all i, and p TD p TD p TD p for all k, and p for all i, and p for all k, and p for all j, and p for all l, and p for all j, and p for all l, and p AF Output for each type of power plant is less then or equal capacity X availability factor (AF) X duration of load block availablet ime calenderperiod operatingtime standbay calenderperiod

27 27 b. Primary energy supply constraint fossil fuel I P ( OutF ip i 1 p 1 K P OutFP ) ( OutNEWF OutNEWFP ) req. fos fuelcons ip kp kp k 1p 1 Output from fossil or non-fossil type power plant (old and new) is less than or equal the fuel consumption (we convert from BOE into MWh) after we adjust for energy conversion. Data we obtain from the Energy Balance Statistics c. Primary energy supply constraint non fossil fuel J P ( OutNF jp j 1p 1 L P ( OutNEWNF lp l 1 p 1 OutNFP jp ) OutNEWNFP lp ) req. nonfos primaryenerg

28 28 d. Demand satisfaction I K ( OutF OutFP ) ( OutNEWF ip ip kp i 1 k 1 J L ( OutNF OutNFP ) ( OutNEWNF jp jp lp j 1 l 1 Output needs to meet demand at all block OutNEWFP kp ) OutNEWNFP lj ) PD p Demand side management (DSM) 5% & 10% reduction load shift e. Contract agreement for fossil I P K P J P OutFP OutNEWFP OutNFP ip kp jp i 1 p 1 k 1 p j 1 p 1 J P j 1 p OutNEWNFP kp I P K P ( OutF OutNEWF ip kp i 1 p 1 k 1 p 1 J P L P OutNF jp j 1 p 1 l 1 p purchase OutNEWNF ) jp At least 19 %

29 29 f. Contract agreement for non-fossil J P OutNFP jp j 1 p 1 L P l 1 p OutNEWNFP lp purchaserew I P K P J P L P ( OutF OutNEWF OutNF ip kp jp i 1 p 1 k 1 p 1 j 1 p 1 l 1 p Vary in 2010 = 4%; in 2020 = 19% OutNEWNF ) jp g. Promoting renewable energy J ( OutNF jp j 1 I pref ( ( OutF ip i 1 OutNFP jp OutFP ip L ) ( OutNEWNF lp l 1 K ) ( OutNEWF kp k 1 OutNEWNFP lp ) OutNEWNFP kp )) Output from renewable power plant need to be above certain level planner preference Vary in 2010 = 12% and in 2020 = 17%

30 30 (3) Objective functions Minimizing generating cost (in Rupiah) I P J P I P MinimizeZ VCF OutF VCNF OutNF VCFP OutFP 1 i ip j jp i ip i 1 p 1 j 1 p 1 i 1 p 1 J P VCNFP j j 1 p 1 K P VCFNP k k 1 p 1 K P OutNFP VCFN jp k k 1 p 1 L P OutNEWFP VCNFNP kp l l 1 p 1 L P OutNEWF VCNFN kp l l 1 p 1 OutNEWNFP lp OutNEWNF lp

31 31 Calculating emission intensity 1. Obtaining output from each type of fossil fuel power plant. 2. Calculating emissions intensity for old power plant and new power plant with the following formula: Emission I P intensity EI i i 1 p 1 ( OutF ip OutFP ip K P ) EI k k 1 p 1 ( OutNEWF kp OutNEWFP kp ) Ei i = emissions intensity (ton CO 2 /MWh) for old fossil power plants Ei k = emissions intensity (ton CO 2 /MWh) for new fossil power plants

32 32 Formulation on SA The dynamic based over the years is defined as follows (IEA, 2009): CB t A CO 2 / MWht existing (1 A) CO 2 / MWh t newplants where CB = crediting baseline, A = weight for the existing capacity The basic assumption is a crediting baseline would apply only to new power plants, with an aims to trigger as much transformation as possible for the new power investment and to reduce the carbon lock in related to new power demand growth

33 33 Steps in calculating carbon credit 1. Measure emission intensity both for old and new plant with three types of steam technology (subcritical, supercritical, ultrasupercritical). 2. Calculate crediting baseline following the given formula (suppose we select A = 0.2; 0.4; 0.6 following IEA for China) for each type of technology. 3. Compare crediting baseline (A = 0.2; 0.4; 0.6) with corresponding crediting baseline when A = 0 (we only take into account new plant). 4. When crediting baseline A = 0 is less than crediting baseline (when A = 0.2; 0.4, 0.6), developing country can obtain carbon credit.

34 34 5. Numerical Results and Sensitivity Analyses

35 35 Electricity production and renewable energy Electricity production (GWh) Share of renewable energy to total production (%) Share of IPP to total Year A B C A B C production , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Note: A = without demand side management; B = with 5% demand side management; C = with 10% demand side management

36 36 Total CO 2 Emissions in million ton Year Subcritical Supercritical Ultrasupercritical A B C A B C A B C % change Note: A = without demand side management; B = with 5% demand side management; C = with 10% demand side management

37 37 Emissions intensity (ton/mwh) Year Subcritical Supercritical Ultrasupercritical A B C A B C A B C Note: A = without demand side management; B = with 5% demand side management; C = with 10% demand side management When emissions intensity increase while we implement the demand side management, this indicates that there is no space for less emitter power plant with lower generating cost into the system. This may indicate carbon lock in situation.

38 38 Generating cost (Rp/kWh) before fuel cost increase Subcritical Supercritical Ultrasupercritical Year A B C A B C A B C Generating cost (Rp/kWh) if gas and coal price increase by 100% Year Subcritical Supercritical Ultrasupercritical A B C A B C A B C Generating cost increase marginally (1.3%/year). Technology premium 0.7% for supercritical, 1.2% for ultrasupercritical, and 0.5% between supercritical and ultrasupercritical. 5% and 10% DSM leads to 7% dan 13% reduction in generating cost. If fuel cost increase, average generating cost for subcritical, supercritical and ultrasupercritical increase by 18%, 16% and 14% respectively.

39 39 Emission credit WITHOUT DSM Year Emissions intensity (tonco2/mwh) Baseline ultrasupercritical Electricity production (GWh) w/o DSM Emission credit (thousand ton CO2) A = 0.6 A = 0.4 A = 0.2 A = 0 A = 0.2 A = 0.4 A = 0.6 Total Emission (Ton CO2) BAU , ,515, , , ,646, , ,003, , ,504 2, ,614, ,400 3,121 6,243 9, ,944, ,461 2,775 5,549 8, ,899, ,939 2,551 5,102 7, ,065, ,787 4,192 8,384 12, ,204, ,839 3,042 6,083 9, ,688, ,512 5,506 11,012 16, ,763,933 Average Cumulative emissions credit 22,366 44,732 67,098 Share of emissions reduction from total emission in 2019 (%) Emissions credit = (emission intensity at corresponding A emission intensity when A=0) x electricity production; we do not obtain carbon credit in 2010 because emissions intensity at corresponding A =0 is higher than A = 0.2; 0.4; and 0.6. BAU = emissions without demand side management under subcritical scenario.

40 40 Emission credit WITH 5% DSM Year Emissions intensity (tonco2/mwh) Baseline-ultrasupercritical Electricity production (GWh) w 5% DSM Emission credit ( thousand ton CO2) A = 0.6 A = 0.4 A = 0.2 A=0 A=0.2 A=0.4 A=0.6 Total Emission (Ton CO2) BAU , ,725, , ,011 1, ,347, , , ,557, , ,852 2, ,308, ,529 2,264 4,527 6, ,735, ,388 2,762 5,523 8, ,156, ,592 2,282 4,563 6, ,078, ,347 3,427 6,853 10, ,906, ,247 2,611 5,222 7, ,004, ,736 4,191 8,381 12, ,230,261 Average Cumulative emissions credit ,689 58,033 Share of emissions reduction from total emission in 2019 (%) Emissions credit = (emission intensity at corresponding A emission intensity when A=0) x electricity production; we do not obtain carbon credit in 2010 because emissions intensity at corresponding A =0 is higher than A = 0.2; 0.4; and 0.6. BAU = emissions with 5% demand side management under subcritical scenario.

41 41 Emission credit WITH 10% DSM Year Emissions intensity (tonco2/mwh) Baseline -ultrasupercritical Electricity production (GWh) w 10% DSM Emission credit (thousand ton CO2) A = 0.6 A = 0.4 A = 0.2 A=0 A=0.2 A=0.4 A=0.6 Total Emission (Ton CO2) BAU , ,194, , ,192 1, ,823, , ,686, , ,351 2, ,993, ,660 1,051 2,101 3, ,828, ,315 2,030 4,059 6, ,960, ,245 1,133 2,267 3, ,728, ,908 1,671 3,342 5, ,529, , ,488, ,961 1,045 2,091 3, ,435,070 Average Cumulative emissions credit 8,413 16,826 25,239 Share of emissions reduction from total emission in 2019 (%) Emissions credit = (emission intensity at corresponding A emission intensity when A=0) x electricity production; we do not obtain carbon credit in 2010 because emissions intensity at corresponding A =0 is higher than A = 0.2; 0.4; and 0.6. BAU = emissions with 10% demand side management under subcritical scenario.

42 42 Carbon credits, steam coal technology and DSM 1. Indonesia can obtain carbon credit by promoting steam-ultrasupercritical technology because it has lower carbon emissions intensity than the old steam power plant and other types of steam coal technology. 2. DSM decreases the crediting baseline (for all A ) 3. DSM reduce emissions credit reduce the cumulative emission credits 4. DSM reduce share of emissions reduction from total emissions 5. Thus besides negotiating parameter A, it is also important to investigate state of demand side management policy. Because this significantly affect the carbon credits.

43 6. Conclusions, Policy Implications and Future Study 43

44 44 Conclusions 1. Reduction on total emissions of CO 2 will depends on three major factors: (i) the magnitude of DSM; (ii) type of steam coal technology; (iii) and share of renewable energy. 2. If price of fossil fuel increase, renewable energy will have better opportunity to operate. 3. Emission intensity tend to decrease for three reasons: (i) share of natural gas increase; (ii) new power plant more efficient in energy use; (iii) share of renewable energy increase. 4. Indonesia can actively participate on SA if new steam power plants use ultrasupercritical technology. Technology switching from subcritical to ultrasupercritical will have minor impact on generating cost, even when fuel costs increase, more advanced technology becomes more competitive than conventional technology 5. Government needs to choose the less stringent the baseline or A = 0.6 (for example) because it has the highest cumulative emissions reduction that is about 67 million ton or about 27.3% reduction from business as usual scenario (BAU). 6. Because the emissions reduction credits (ERCs) is very sensitive to parameter A and the parameter of DSM, it important to set up energy intensity benchmarking that is developed by independent experts (Schmidt et al, 2008).

45 45 Policy Implications & Future Study We suggest that Indonesia needs to adopt more advanced technology on steam coal power plant. Demand side management needs to be improved by developing capacity building in terms of monitoring, evaluating and enforcement. Specific policies need to be prepared to reduce investment risks and to provide more attractive long term contract for renewable energy. Because share of IPP will increase in the future, government also needs to set emission intensity standard. Indonesia needs to pursue more broad and comprehensive strategy on CO2 emissions from the power sector such as simultaneously implement renewable targeting, NAP, DSM, and SA. The important of finance and assistance package to deploy advanced technologies (Schmidt et al, 2008). Future study: we also need to investigate possibility of carbon credits by implementing several scenarios such as more ambitious target on renewable energy investment, carbon capture and storage (CCS), change the structure of fossil fuel consumption toward gas instead of coal and oil, and proposing nuclear power generation into the model.

46 Domo arigatou gozaimasu 46